Diverse ranking metamaterial inverse design based on contrastive and transfer learning
Posted on 2023-09-18 - 18:42
Metamaterials, thoughtfully designed, have demonstrated remarkable success in the manipulation of electromagnetic waves. More recently, deep learning can advance the performance in the field of metamaterial inverse design. However, existing inverse design methods based on deep learning often overlook potential trade-offs of optimal design and outcome diversity. To address this issue, in this work we introduce contrastive learning to implement a simple but effective global ranking inverse design framework. Viewing inverse design as spectrum-guided ranking of the candidate structures, our method creates a resemblance relationship of the optical response and metamaterials, enabling the prediction of diverse structures of metamaterials based on the global ranking. Furthermore, we have combined transfer learning to enrich our framework, not limited in prediction of single metamaterial representation. Our work can offer inverse design evaluation and diverse outcomes. The proposed method may shrink the gap between flexibility and accuracy of on-demand design.
CITE THIS COLLECTION
Deng, Zhengwei; Li, Yuxiang; Li, Yicheng; Wang, Yiyuan; Li, Wenjia; Zhu, Zheng; et al. (2023). Diverse ranking metamaterial inverse design based on contrastive and transfer learning. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.6764307.v2
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AUTHORS (8)
ZD
Zhengwei Deng
YL
Yuxiang Li
YL
Yicheng Li
YW
Yiyuan Wang
WL
Wenjia Li
ZZ
Zheng Zhu
CG
Chunying Guan
JS
Jin-hui Shi